Towards a Real-Time Minimally-Invasive Vascular Intervention Simulation System

Recently, foundations rooted in physics have been laid down for the goal of simulating the propagation of a guide wire inside the vasculature. At the heart of the simulation lies the fundamental task of energy minimization. The energy comes from interaction with the vessel wall and the bending of the guide wire. For the simulation to be useful in actual training, obtaining the smallest possible optimization time is key. In this paper, we, therefore, study the influence of using different optimization techniques: a semianalytical approximation algorithm, the conjugate-gradients algorithm, and an evolutionary algorithm (EA), specifically the GLIDE algorithm. Simulation performance has been measured on phantom data. The results show that a substantial reduction in time can be obtained while the error is increased only slightly if conjugate gradients or GLIDE is used

[1]  Wiro J Niessen,et al.  Simulation of minimally invasive vascular interventions for training purposes† , 2004, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[2]  Reinhard Männer,et al.  CathI — catheter instruction system , 2002 .

[3]  S. Cotin,et al.  Designing a computer‐based simulator for interventional cardiology training , 2000, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[4]  Andrea Giachetti,et al.  ViVa: the virtual vascular project , 1998, IEEE Transactions on Information Technology in Biomedicine.

[5]  S Sankar,et al.  Training environment for inferior vena caval filter placement. , 1998, Studies in health technology and informatics.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Jürgen Hesser,et al.  CathI - Training System for PTCA. A Step Closer to Reality , 2004, ISMS.

[8]  W. Niessen,et al.  Analytical guide wire motion algorithm for simulation of endovascular interventions , 2003, Medical and Biological Engineering and Computing.

[9]  G. Arfken.  Mathematical Methods for Physicists , 1967 .

[10]  Chee-Kong Chui,et al.  Training and pretreatment planning of interventional neuroradiology procedures--initial clinical validation. , 2002, Studies in health technology and informatics.

[11]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[12]  C. Chui,et al.  Real-time interactive simulator for percutaneous coronary revascularization procedures. , 1998, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[13]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[14]  James H. Anderson,et al.  Computer Environment for Interventional Neuroradiology Procedures , 2001 .

[15]  Chee-Kong Chui,et al.  Virtual reality training in interventional radiology: The Johns Hopkins and Kent Ridge digital laboratory experience , 2002 .

[16]  Peter A. N. Bosman,et al.  Design and Application of iterated Density-Estimation Evolutionary Algorithms , 2003 .

[17]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[18]  E. Gobbetti,et al.  Catheter insertion simulation with co-registered direct volume rendering and haptic feedback. , 2000, Studies in health technology and informatics.